code: def merge_feature(input, rest):
      # merge the splitted features into full utterance
      # input is the features: (B, N, L, K)
  
      batch_size, dim, segment_size, _ = input.shape
      segment_stride = segment_size // 2
      input = (
          input.transpose(2, 3)
          .contiguous()
          .view(batch_size, dim, -1, segment_size * 2)
      )  # B, N, K, L
  
      input1 = (
          input[:, :, :, :segment_size]
          .contiguous()
          .view(batch_size, dim, -1)[:, :, segment_stride:]
      )
      input2 = (
          input[:, :, :, segment_size:]
          .contiguous()
          .view(batch_size, dim, -1)[:, :, :-segment_stride]
      )
  
      output = input1 + input2
      if rest > 0:
          output = output[:, :, :-rest]
  
      return output.contiguous()
  
file_path: espnet/espnet2/enh/layers/dprnn.py
imports: import torch
  import torch.nn
  from torch.autograd import Variable
label_desc: Use image node_id 3 for calling a global function with example usage: merge_feature(input, rest) and returns: output